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1 ExpressSummary

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1.1 Sample & Experimental Context

Species Disease Tissue
human normal blood

1.2 Cell Count & Expression Summary

2 ExpressSettings

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Argument Description Entered Value
config Path to JSON config file. None
species Species type used in the analysis. hs
tissue Tissue type for the dataset. blood
disease Disease name normal
min_umi_per_cell Minimum UMI counts required per cell. {‘S1’: 750, ‘S2’: 750, ‘S3’: 750, ‘S4’: 750, ‘S5’: 750, ‘S6’: 750, ‘S7’: 750, ‘S8’: 750, ‘S9’: 750, ‘S10’: 750, ‘S11’: 750, ‘S12’: 750, ‘S13’: 750, ‘S14’: 750, ‘S15’: 750, ‘S16’: 750, ‘S17’: 750, ‘S18’: 750}
max_umi_per_cell Maximum UMI counts allowed per cell. {‘S1’: inf, ‘S2’: inf, ‘S3’: inf, ‘S4’: inf, ‘S5’: inf, ‘S6’: inf, ‘S7’: inf, ‘S8’: inf, ‘S9’: inf, ‘S10’: inf, ‘S11’: inf, ‘S12’: inf, ‘S13’: inf, ‘S14’: inf, ‘S15’: inf, ‘S16’: inf, ‘S17’: inf, ‘S18’: inf}
min_genes_per_cell Minimum number of detected genes per cell. {‘S1’: 250, ‘S2’: 250, ‘S3’: 250, ‘S4’: 250, ‘S5’: 250, ‘S6’: 250, ‘S7’: 250, ‘S8’: 250, ‘S9’: 250, ‘S10’: 250, ‘S11’: 250, ‘S12’: 250, ‘S13’: 250, ‘S14’: 250, ‘S15’: 250, ‘S16’: 250, ‘S17’: 250, ‘S18’: 250}
max_genes_per_cell Maximum number of detected genes per cell. {‘S1’: inf, ‘S2’: inf, ‘S3’: inf, ‘S4’: inf, ‘S5’: inf, ‘S6’: inf, ‘S7’: inf, ‘S8’: inf, ‘S9’: inf, ‘S10’: inf, ‘S11’: inf, ‘S12’: inf, ‘S13’: inf, ‘S14’: inf, ‘S15’: inf, ‘S16’: inf, ‘S17’: inf, ‘S18’: inf}
min_cell Minimum number of cells in which a gene must be detected to be retained. {‘S1’: 3, ‘S2’: 3, ‘S3’: 3, ‘S4’: 3, ‘S5’: 3, ‘S6’: 3, ‘S7’: 3, ‘S8’: 3, ‘S9’: 3, ‘S10’: 3, ‘S11’: 3, ‘S12’: 3, ‘S13’: 3, ‘S14’: 3, ‘S15’: 3, ‘S16’: 3, ‘S17’: 3, ‘S18’: 3}
max_mt_percent Maximum allowed percentage of mitochondrial content per cell. {‘S1’: 15.0, ‘S2’: 15.0, ‘S3’: 15.0, ‘S4’: 15.0, ‘S5’: 15.0, ‘S6’: 15.0, ‘S7’: 15.0, ‘S8’: 15.0, ‘S9’: 15.0, ‘S10’: 15.0, ‘S11’: 15.0, ‘S12’: 15.0, ‘S13’: 15.0, ‘S14’: 15.0, ‘S15’: 15.0, ‘S16’: 15.0, ‘S17’: 15.0, ‘S18’: 15.0}
doublet_method Method used for doublet identification. scrublet
scrublet_cutoff Threshold for filtering doublets based on Scrublet score. 0.25
norm_target_sum Target sum for normalization, defining the total counts per cell. 10000.0
n_top_genes Number of highly variable genes to retain for downstream analysis. 2000
regress_out Whether total counts and mitochondrial percentages are regressed out. yes
scale_max_value Maximum value for data scaling. 10.0
n_pcs Number of principal components used in PCA. 30
batch_correction Batch correction method applied. harmony
batch_vars Metadata column(s) used for batch correction. sample_id
n_neighbors Number of neighbors used for kNN graph construction. 15
resolution Resolution parameter for Leiden clustering. 0.6
compute_tsne Indicates whether t-SNE embedding is computed. yes
annotation_method Method used for cell annotation. scimilarity,celltypist
sci_model_path Path to the SCimilarity model for annotation. /mnt/work/projects/cellatria/data/scimilarity/model_v1.1
cty_model_path Path to the Celltypist model for annotation. /mnt/work/projects/cellatria/data/celltypist/model_v1.6.3
cty_model_name Celltypist model used. Immune_All_Low
pval_threshold P-value threshold for filtering differentially expressed genes (DEGs). 0.05
logfc_threshold Log fold-change (logFC) threshold for filtering DEGs. 0.25
dea_method Statistical test used for differential expression analysis. wilcoxon
top_n_deg_leidn Number of top differentially expressed genes (DEGs) to return per ‘leiden’ clusters. 100
top_n_deg_scim Number of top differentially expressed genes (DEGs) to return per ‘scimilarity’ annotated cells. 100
top_n_deg_cltpst Number of top differentially expressed genes (DEGs) to return per ‘celltypist’ annotated cells. 100
pts_threshold Minimum fraction of cells expressing a gene for it to be considered a DEG. 0.1
fix_gene_names Fix gene names if Ensembl IDs are detected. no
limit_threads Apply thread limits to avoid memory crashes 1
plot_alpha Opacity level for projection plots. 0.6

3 ExpressQC

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3.1 Sample-level Characteristics and Quality Control Metrics

3.1.1 Metadata

sample sample_id
GSM6189249 S1
GSM6189250 S2
GSM6189251 S3
GSM6189252 S4
GSM6189253 S5
GSM6189254 S6
GSM6189255 S7
GSM6189256 S8
GSM6189257 S9
GSM6189258 S10
GSM6189259 S11
GSM6189260 S12
GSM6189261 S13
GSM6189262 S14
GSM6189263 S15
GSM6189264 S16
GSM6189265 S17
GSM6189266 S18

3.1.2 Cells & Genes

sample sample_id pre_qc_gene pre_qc_cell post_qc_gene post_qc_cell
GSM6189249 S1 20,891 6,921 17,382 3,994
GSM6189250 S2 21,246 10,918 18,086 6,737
GSM6189251 S3 21,339 8,433 18,160 6,327
GSM6189252 S4 21,255 10,241 17,963 6,710
GSM6189253 S5 20,769 8,359 16,376 3,249
GSM6189254 S6 21,476 11,364 18,252 8,226
GSM6189255 S7 20,062 5,151 16,728 3,047
GSM6189256 S8 18,979 3,523 15,416 1,860
GSM6189257 S9 20,485 5,156 16,916 2,971
GSM6189258 S10 19,545 4,100 16,021 2,380
GSM6189259 S11 20,467 6,138 17,047 3,795
GSM6189260 S12 20,364 5,098 16,870 3,098
GSM6189261 S13 20,047 4,475 16,789 3,005
GSM6189262 S14 18,662 1,459 15,213 919
GSM6189263 S15 19,920 5,125 16,673 3,139
GSM6189264 S16 20,472 5,523 17,079 3,688
GSM6189265 S17 20,914 5,348 17,513 3,854
GSM6189266 S18 20,554 6,159 17,091 4,102

3.1.3 UMI per cell

Vales are post-QC.

sample sample_id min q0 q25 q50 q75 q100 max
GSM6189249 S1 764 764 5,237 6,616 8,143 27,116 27,116
GSM6189250 S2 757 757 4,655 5,847 7,181 43,566 43,566
GSM6189251 S3 756 756 4,944 6,186 7,588 58,054 58,054
GSM6189252 S4 761 761 4,529 5,646 7,056 57,462 57,462
GSM6189253 S5 750 750 1,265 2,076 3,974 52,899 52,899
GSM6189254 S6 751 751 4,149 5,382 6,844 42,904 42,904
GSM6189255 S7 786 786 5,162 6,700 8,226 29,128 29,128
GSM6189256 S8 750 750 3,684 5,716 7,362 53,772 53,772
GSM6189257 S9 758 758 5,026 6,795 8,512 61,753 61,753
GSM6189258 S10 751 751 3,812 6,080 7,905 84,052 84,052
GSM6189259 S11 751 751 4,285 5,935 7,516 99,574 99,574
GSM6189260 S12 753 753 4,909 6,483 8,129 45,268 45,268
GSM6189261 S13 760 760 4,204 5,564 6,747 32,149 32,149
GSM6189262 S14 991 991 5,843 7,439 9,277 41,415 41,415
GSM6189263 S15 755 755 4,719 5,977 7,332 38,681 38,681
GSM6189264 S16 751 751 4,347 5,986 7,525 82,498 82,498
GSM6189265 S17 752 752 4,518 6,134 8,228 89,182 89,182
GSM6189266 S18 753 753 4,012 5,414 6,914 40,118 40,118

3.1.4 Gene per cell

Vales are post-QC.

sample sample_id min q0 q25 q50 q75 q100 max
GSM6189249 S1 303 303 1,565 1,779 2,022 4,621 4,621
GSM6189250 S2 405 405 1,549 1,784 2,052 5,429 5,429
GSM6189251 S3 346 346 1,522 1,726 1,966 5,622 5,622
GSM6189252 S4 367 367 1,395 1,603 1,888 6,527 6,527
GSM6189253 S5 251 251 725 1,056 1,546 6,306 6,306
GSM6189254 S6 253 253 1,372 1,576 1,824 5,595 5,595
GSM6189255 S7 336 336 1,472 1,692 1,930 4,703 4,703
GSM6189256 S8 352 352 1,272 1,622 1,918 6,641 6,641
GSM6189257 S9 257 257 1,536 1,798 2,064 5,852 5,852
GSM6189258 S10 258 258 1,281 1,635 1,898 8,324 8,324
GSM6189259 S11 250 250 1,303 1,591 1,878 7,383 7,383
GSM6189260 S12 276 276 1,442 1,688 1,957 5,130 5,130
GSM6189261 S13 290 290 1,322 1,541 1,769 5,030 5,030
GSM6189262 S14 449 449 1,634 1,904 2,200 5,739 5,739
GSM6189263 S15 250 250 1,339 1,541 1,766 5,068 5,068
GSM6189264 S16 254 254 1,341 1,570 1,880 7,114 7,114
GSM6189265 S17 259 259 1,474 1,802 2,289 6,891 6,891
GSM6189266 S18 261 261 1,260 1,492 1,768 5,273 5,273

3.1.5 Mitochondrial (%)

Vales are post-QC.

sample sample_id min q0 q25 q50 q75 q100 max
GSM6189249 S1 0.0% 0.0% 6.1% 7.3% 8.9% 15.0% 15.0%
GSM6189250 S2 0.3% 0.3% 4.6% 5.6% 6.9% 15.0% 15.0%
GSM6189251 S3 0.1% 0.1% 5.8% 6.9% 8.2% 15.0% 15.0%
GSM6189252 S4 0.1% 0.1% 6.7% 7.9% 9.3% 15.0% 15.0%
GSM6189253 S5 0.0% 0.0% 3.7% 6.2% 9.9% 15.0% 15.0%
GSM6189254 S6 0.0% 0.0% 4.6% 5.5% 6.8% 15.0% 15.0%
GSM6189255 S7 0.1% 0.1% 4.6% 5.6% 7.1% 15.0% 15.0%
GSM6189256 S8 0.1% 0.1% 3.7% 4.7% 6.4% 14.9% 14.9%
GSM6189257 S9 0.0% 0.0% 4.1% 5.1% 6.7% 15.0% 15.0%
GSM6189258 S10 0.1% 0.1% 4.0% 5.2% 7.2% 15.0% 15.0%
GSM6189259 S11 0.0% 0.0% 3.7% 4.7% 6.2% 15.0% 15.0%
GSM6189260 S12 0.0% 0.0% 4.1% 5.0% 6.6% 15.0% 15.0%
GSM6189261 S13 0.3% 0.3% 5.5% 6.7% 8.1% 15.0% 15.0%
GSM6189262 S14 0.0% 0.0% 4.6% 5.6% 7.0% 14.9% 14.9%
GSM6189263 S15 0.0% 0.0% 5.1% 6.3% 7.7% 15.0% 15.0%
GSM6189264 S16 0.0% 0.0% 5.7% 7.0% 8.7% 15.0% 15.0%
GSM6189265 S17 0.0% 0.0% 4.5% 5.5% 6.7% 15.0% 15.0%
GSM6189266 S18 0.2% 0.2% 5.2% 6.5% 8.3% 15.0% 15.0%

3.2 Distribution Plots for Quality Control Metrics

Thresholds, represented by dark-red dashed lines, were implemented to filter the data and only retain cells of high quality.

3.2.1 UMI per cell distribution

3.2.2 Gene per cell distribution

3.2.3 Mitochondrial (%) per cell distribution

3.2.4 Total UMI Counts vs. Genes Detected per cell

The gray dashed line indicates the identity line (y = x)

3.3 Barcodes Contamination

3.3.1 Count - before QC

Number of barcodes shared between pairs of samples pre-QC.

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18
S1 6921 18 21 25 10 20 13 5 8 5 12 8 13 3 7 12 12 9
S2 18 10918 24 30 30 23 12 7 21 20 23 13 13 5 21 15 15 17
S3 21 24 8433 12 15 31 14 15 4 7 16 14 11 5 11 9 8 8
S4 25 30 12 10241 22 38 10 5 11 12 23 16 10 5 23 11 14 18
S5 10 30 15 22 8359 33 11 10 13 8 13 12 12 1 10 11 8 20
S6 20 23 31 38 33 11364 16 14 14 14 22 19 10 4 18 15 18 27
S7 13 12 14 10 11 16 5151 2 6 4 10 6 9 2 7 8 9 8
S8 5 7 15 5 10 14 2 3523 4 8 4 3 7 0 6 7 3 4
S9 8 21 4 11 13 14 6 4 5156 8 5 1 6 1 2 6 10 8
S10 5 20 7 12 8 14 4 8 8 4100 7 8 3 1 10 6 10 7
S11 12 23 16 23 13 22 10 4 5 7 6138 8 8 4 14 10 11 11
S12 8 13 14 16 12 19 6 3 1 8 8 5098 8 7 6 10 4 5
S13 13 13 11 10 12 10 9 7 6 3 8 8 4475 1 4 9 7 11
S14 3 5 5 5 1 4 2 0 1 1 4 7 1 1459 4 2 2 5
S15 7 21 11 23 10 18 7 6 2 10 14 6 4 4 5125 8 4 17
S16 12 15 9 11 11 15 8 7 6 6 10 10 9 2 8 5523 10 15
S17 12 15 8 14 8 18 9 3 10 10 11 4 7 2 4 10 5348 49
S18 9 17 8 18 20 27 8 4 8 7 11 5 11 5 17 15 49 6159

3.3.2 Count - post QC

Number of barcodes shared between pairs of samples post-QC.

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18
S1 3994 7 10 10 3 10 4 1 5 1 6 6 6 2 2 4 4 4
S2 7 6737 12 12 6 11 6 2 10 5 5 6 5 3 11 2 3 6
S3 10 12 6327 5 6 18 8 7 0 5 7 9 5 4 5 3 5 2
S4 10 12 5 6710 5 20 5 1 6 4 9 7 5 2 10 4 10 3
S5 3 6 6 5 3249 13 2 1 4 2 3 1 4 0 3 5 0 5
S6 10 11 18 20 13 8226 6 7 4 4 11 6 3 2 12 6 11 14
S7 4 6 8 5 2 6 3047 1 1 0 2 3 5 0 2 3 4 2
S8 1 2 7 1 1 7 1 1860 1 3 0 2 2 0 0 2 2 1
S9 5 10 0 6 4 4 1 1 2971 1 2 0 2 0 1 1 5 2
S10 1 5 5 4 2 4 0 3 1 2380 2 3 1 1 3 0 3 3
S11 6 5 7 9 3 11 2 0 2 2 3795 2 2 2 3 5 5 4
S12 6 6 9 7 1 6 3 2 0 3 2 3098 3 1 1 3 0 4
S13 6 5 5 5 4 3 5 2 2 1 2 3 3005 0 1 3 4 9
S14 2 3 4 2 0 2 0 0 0 1 2 1 0 919 2 0 2 2
S15 2 11 5 10 3 12 2 0 1 3 3 1 1 2 3139 5 2 8
S16 4 2 3 4 5 6 3 2 1 0 5 3 3 0 5 3688 3 4
S17 4 3 5 10 0 11 4 2 5 3 5 0 4 2 2 3 3854 1
S18 4 6 2 3 5 14 2 1 2 3 4 4 9 2 8 4 1 4102

3.3.3 Jaccard Index - before QC

Fraction (%) of barcodes shared between pairs of samples pre-QC.

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18
S1 100.00 0.20 0.27 0.29 0.13 0.22 0.22 0.10 0.13 0.09 0.18 0.13 0.23 0.07 0.12 0.19 0.20 0.14
S2 0.20 100.00 0.25 0.28 0.31 0.21 0.15 0.10 0.26 0.27 0.27 0.16 0.17 0.08 0.26 0.18 0.18 0.20
S3 0.27 0.25 100.00 0.13 0.18 0.31 0.21 0.25 0.06 0.11 0.22 0.21 0.17 0.10 0.16 0.13 0.12 0.11
S4 0.29 0.28 0.13 100.00 0.24 0.35 0.13 0.07 0.14 0.17 0.28 0.21 0.14 0.09 0.30 0.14 0.18 0.22
S5 0.13 0.31 0.18 0.24 100.00 0.33 0.16 0.17 0.19 0.13 0.18 0.18 0.19 0.02 0.15 0.16 0.12 0.28
S6 0.22 0.21 0.31 0.35 0.33 100.00 0.19 0.19 0.17 0.18 0.25 0.23 0.13 0.06 0.22 0.18 0.22 0.31
S7 0.22 0.15 0.21 0.13 0.16 0.19 100.00 0.05 0.12 0.09 0.18 0.12 0.19 0.06 0.14 0.15 0.17 0.14
S8 0.10 0.10 0.25 0.07 0.17 0.19 0.05 100.00 0.09 0.21 0.08 0.07 0.18 0.00 0.14 0.15 0.07 0.08
S9 0.13 0.26 0.06 0.14 0.19 0.17 0.12 0.09 100.00 0.17 0.09 0.02 0.12 0.03 0.04 0.11 0.19 0.14
S10 0.09 0.27 0.11 0.17 0.13 0.18 0.09 0.21 0.17 100.00 0.14 0.17 0.07 0.04 0.22 0.12 0.21 0.14
S11 0.18 0.27 0.22 0.28 0.18 0.25 0.18 0.08 0.09 0.14 100.00 0.14 0.15 0.11 0.25 0.17 0.19 0.18
S12 0.13 0.16 0.21 0.21 0.18 0.23 0.12 0.07 0.02 0.17 0.14 100.00 0.17 0.21 0.12 0.19 0.08 0.09
S13 0.23 0.17 0.17 0.14 0.19 0.13 0.19 0.18 0.12 0.07 0.15 0.17 100.00 0.03 0.08 0.18 0.14 0.21
S14 0.07 0.08 0.10 0.09 0.02 0.06 0.06 0.00 0.03 0.04 0.11 0.21 0.03 100.00 0.12 0.06 0.06 0.13
S15 0.12 0.26 0.16 0.30 0.15 0.22 0.14 0.14 0.04 0.22 0.25 0.12 0.08 0.12 100.00 0.15 0.08 0.30
S16 0.19 0.18 0.13 0.14 0.16 0.18 0.15 0.15 0.11 0.12 0.17 0.19 0.18 0.06 0.15 100.00 0.18 0.26
S17 0.20 0.18 0.12 0.18 0.12 0.22 0.17 0.07 0.19 0.21 0.19 0.08 0.14 0.06 0.08 0.18 100.00 0.85
S18 0.14 0.20 0.11 0.22 0.28 0.31 0.14 0.08 0.14 0.14 0.18 0.09 0.21 0.13 0.30 0.26 0.85 100.00

3.3.4 Jaccard Index - post QC

Fraction (%) of barcodes shared between pairs of samples post-QC.

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18
S1 100.00 0.13 0.19 0.19 0.08 0.16 0.11 0.03 0.14 0.03 0.15 0.17 0.17 0.08 0.06 0.10 0.10 0.10
S2 0.13 100.00 0.18 0.18 0.12 0.15 0.12 0.05 0.21 0.11 0.09 0.12 0.10 0.08 0.22 0.04 0.06 0.11
S3 0.19 0.18 100.00 0.08 0.13 0.25 0.17 0.17 0.00 0.11 0.14 0.19 0.11 0.11 0.11 0.06 0.10 0.04
S4 0.19 0.18 0.08 100.00 0.10 0.27 0.10 0.02 0.12 0.09 0.17 0.14 0.10 0.05 0.20 0.08 0.19 0.06
S5 0.08 0.12 0.13 0.10 100.00 0.23 0.06 0.04 0.13 0.07 0.09 0.03 0.13 0.00 0.09 0.14 0.00 0.14
S6 0.16 0.15 0.25 0.27 0.23 100.00 0.11 0.14 0.07 0.08 0.18 0.11 0.05 0.04 0.21 0.10 0.18 0.23
S7 0.11 0.12 0.17 0.10 0.06 0.11 100.00 0.04 0.03 0.00 0.06 0.10 0.17 0.00 0.06 0.09 0.12 0.06
S8 0.03 0.05 0.17 0.02 0.04 0.14 0.04 100.00 0.04 0.14 0.00 0.08 0.08 0.00 0.00 0.07 0.07 0.03
S9 0.14 0.21 0.00 0.12 0.13 0.07 0.03 0.04 100.00 0.04 0.06 0.00 0.07 0.00 0.03 0.03 0.15 0.06
S10 0.03 0.11 0.11 0.09 0.07 0.08 0.00 0.14 0.04 100.00 0.06 0.11 0.04 0.06 0.11 0.00 0.10 0.09
S11 0.15 0.09 0.14 0.17 0.09 0.18 0.06 0.00 0.06 0.06 100.00 0.06 0.06 0.08 0.09 0.13 0.13 0.10
S12 0.17 0.12 0.19 0.14 0.03 0.11 0.10 0.08 0.00 0.11 0.06 100.00 0.10 0.05 0.03 0.09 0.00 0.11
S13 0.17 0.10 0.11 0.10 0.13 0.05 0.17 0.08 0.07 0.04 0.06 0.10 100.00 0.00 0.03 0.09 0.12 0.25
S14 0.08 0.08 0.11 0.05 0.00 0.04 0.00 0.00 0.00 0.06 0.08 0.05 0.00 100.00 0.10 0.00 0.08 0.08
S15 0.06 0.22 0.11 0.20 0.09 0.21 0.06 0.00 0.03 0.11 0.09 0.03 0.03 0.10 100.00 0.15 0.06 0.22
S16 0.10 0.04 0.06 0.08 0.14 0.10 0.09 0.07 0.03 0.00 0.13 0.09 0.09 0.00 0.15 100.00 0.08 0.10
S17 0.10 0.06 0.10 0.19 0.00 0.18 0.12 0.07 0.15 0.10 0.13 0.00 0.12 0.08 0.06 0.08 100.00 0.03
S18 0.10 0.11 0.04 0.06 0.14 0.23 0.06 0.03 0.06 0.09 0.10 0.11 0.25 0.08 0.22 0.10 0.03 100.00

3.4 Doublet Cell Detection using Scrublet Scoring System

Observed scores are used for doublet classification. Dashed line indicates the threshold used to identify doublets.

3.4.1 Observed scores

3.4.2 Simulated scores

3.5 Impact of Quality Control on Cell, Gene, and UMI Abundances

3.5.1 Number of cells and genes per sample

circle and diamonds refer to before and after QC, respectively.

3.5.2 Average number of UMIs/cell and genes/cell per sample

The error bars represent the standard deviation of the number of UMIs and genes across cells per sample.

4 ExpressObject

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4.1 Key QC Metrics of Merged Object

min q0 q25 q50 q75 q100 max
UMI per cell 750 750 4,358 5,871 7,460 99,574 99,574
Gene per cell 250 250 1,387 1,643 1,936 8,324 8,324
Mitochondrial (%) 0.00 0.00 4.79 6.14 7.85 15.00 15.00

4.2 Dictionary of Computed Metadata Fields and their Descriptions

Metadata Description
sample_id Unique identifier for each sample, used to track each sample.
sample Sample name provided by the user, matching the input file or experimental label.
n_genes_by_counts Number of detected genes per cell
total_counts Total number of UMIs per cell
total_counts_mito Total UMIs from mitochondrial genes per cell
pct_counts_mito Percentage of mitochondrial UMIs per cell
n_genes Number of detected genes per cell
n_counts Total number of UMIs per cell
doublet_scores_obs Scrublet doublet score for each observed cell
predicted_doublet Doublet prediction label (True/False)
leiden_cluster Leiden cluster assignment
cellstate_scimilarity Predicted cell state from scimilarity
celltype_scimilarity Curated cell type using pipeline ontology
cellstate_celltypist Predicted cell state from celltypist
celltype_celltypist Curated cell type using pipeline ontology

4.3 Visualization of Merged scRNA-Seq Object in 2D Space

4.3.1 UMAP Projection

4.3.2 UMAP Projection (no-batch correction)

4.3.3 TSNE Projection

4.3.4 TSNE Projection (no-batch correction)

4.4 Visualizing Cell QC Metrics Using Dimensionality Reduction

4.4.1 UMI per cell - UMAP Projection

4.4.2 Gene per cell - UMAP Projection

4.4.3 Mitochondrial (%) per cell - UMAP Projection

4.4.4 UMI per cell - TSNE Projection

4.4.5 Gene per cell - TSNE Projection

4.4.6 Mitochondrial (%) per cell - TSNE Projection

5 ExpressCluster

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5.1 Visualizing Cell Populations with Dimensionality Reduction

5.1.1 UMAP Projection

5.1.2 UMAP Projection (no-batch correction)

5.1.3 TSNE Projection

5.1.4 TSNE Projection (no-batch correction)

5.2 Cell Composition and Abundance in Clusters

Values at the top of each bar indicate the percentage of cells

5.2.1 Cluster Frequency

5.2.2 Cluster Frequency (no-batch correction)

5.3 Sample Imbalance in Clusters Using Gini Indices

The Gini coefficient is used to quantify sample-level or batch-level imbalance within each Leiden cluster. Higher Gini values indicate skewed representation (e.g., one sample dominating a cluster), which may be indicative of batch effects or sampling artifacts. Values next to each point reflect the number of cells per cluster. A horizontal red dashed line at Gini = 0.5 is provided as a visual threshold to aid interpretation.

5.3.1 Dispersion Curve by ‘sample_id’ (default plotting)

Computed using batch-corrected data (if enabled)

5.3.2 Dispersion Curve by sample_id (no batch correction)

5.3.3 Dispersion Curve by sample_id (after batch correction)

6 ExpressAnnotation

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6.1 Visualizing Distinct Cell Type Populations with Dimensionality Reduction

6.1.1 scimilarity UMAP Projection

6.1.2 scimilarity TSNE Projection

6.1.3 Celltypist UMAP Projection

6.1.4 Celltypist TSNE Projection

6.2 Cell Type Population Frequency

Values at the top of each bar indicate the percentage of cells

6.2.1 SCimilarity (cell type)

6.2.2 SCimilarity (cell state)

6.2.3 CellTypist (cell type)

6.2.4 CellTypist (cell state)

6.3 Cell Type Population Composition

The curated ontology (cell type) acts as a reference framework, while annotation methods refine classifications into more detailed (cell state) categories. The network visualizes hierarchical mapping from cellstate (real annotation) to celltype (curated).

6.3.1 SCimilarity

6.3.2 CellTypist

7 ExpressMarkers

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7.1 Expression Signatures Genes for Each Group

Top markers (p_val_adj < 0.05 and expressed in more than 0.1 fraction of cells) for each group.

7.1.1 Leiden Cluster

7.1.2 SCimilarity Group

7.1.3 CellTypist Group

8 ExpressRefs

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Documentation: View Paper

Data Availability: Access Data

9 ExpressWorkspace

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Computing environment information

9.1 Python

Python Environment (Python 3.12.9 )
Package Version
Brotli 1.1.0
Deprecated 1.2.18
GEOparse 2.0.4
GitPython 3.1.44
MarkupSafe 3.0.2
PyPrind 2.11.3
PySocks 1.7.1
PyYAML 6.0.2
Send2Trash 1.8.3
absl-py 2.3.1
accelerate 1.11.0
aiofiles 24.1.0
aiohappyeyeballs 2.6.1
aiohttp 3.13.2
aiosignal 1.4.0
anndata 0.12.4
annotated-doc 0.0.3
annotated-types 0.7.0
annoy 1.17.3
anthropic 0.72.0
anyio 4.8.0
archspec 0.2.3
argon2-cffi 23.1.0
argon2-cffi-bindings 21.2.0
array-api-compat 1.12.0
arrow 1.3.0
asciitree 0.3.3
asttokens 3.0.0
async-lru 2.0.4
attrs 25.1.0
babel 2.17.0
beautifulsoup4 4.13.3
bioturing-connector 1.14.0
bleach 6.2.0
boltons 24.0.0
cached-property 1.5.2
cachetools 6.2.1
captum 0.8.0
celltypist 1.7.1
certifi 2025.1.31
cffi 1.17.1
charset-normalizer 3.4.0
circlify 0.15.1
click 8.3.0
colorama 0.4.6
comm 0.2.2
conda 25.1.1
conda-libmamba-solver 25.1.1
conda-package-handling 2.3.0
conda-package-streaming 0.10.0
contourpy 1.3.3
cycler 0.12.1
cython 3.1.6
dataclasses-json 0.6.7
debugpy 1.8.12
decorator 5.1.1
defusedxml 0.7.1
distro 1.9.0
docrep 0.3.2
docstring-parser 0.17.0
entmax 1.3
et-xmlfile 2.0.0
exceptiongroup 1.2.2
executing 2.1.0
fa2-modified 0.4
fastapi 0.120.1
fasteners 0.20
fastjsonschema 2.21.1
ffmpy 0.6.4
filelock 3.20.0
fonttools 4.60.1
fqdn 1.5.1
frozendict 2.4.6
frozenlist 1.8.0
fsspec 2025.9.0
gitdb 4.0.12
google-ai-generativelanguage 0.6.15
google-api-core 2.28.1
google-api-python-client 2.185.0
google-auth 2.42.0
google-auth-httplib2 0.2.0
google-generativeai 0.8.5
googleapis-common-protos 1.71.0
gradio 5.49.1
gradio-client 1.13.3
graphviz 0.21
greenlet 3.2.4
groovy 0.1.2
grpcio 1.76.0
grpcio-status 1.71.2
h11 0.14.0
h2 4.1.0
h5py 3.15.1
harmonypy 0.0.10
hf-xet 1.2.0
hnswlib 0.8.0
hpack 4.0.0
httpcore 1.0.7
httplib2 0.31.0
httpx 0.28.1
httpx-sse 0.4.3
huggingface-hub 0.36.0
hyperframe 6.0.1
idna 3.10
igraph 0.11.9
imageio 2.37.0
importlib-metadata 8.6.1
importlib-resources 6.5.2
ipykernel 6.29.5
ipython 8.32.0
isoduration 20.11.0
jedi 0.19.2
jinja2 3.1.5
jiter 0.11.1
joblib 1.5.2
json5 0.10.0
jsonpatch 1.33
jsonpointer 3.0.0
jsonschema 4.23.0
jsonschema-specifications 2024.10.1
jupyter-client 8.6.3
jupyter-core 5.7.2
jupyter-events 0.12.0
jupyter-lsp 2.2.5
jupyter-server 2.15.0
jupyter-server-mathjax 0.2.6
jupyter-server-terminals 0.5.3
jupyterlab 4.3.5
jupyterlab-git 0.51.0
jupyterlab-pygments 0.3.0
jupyterlab-server 2.27.3
jupyterlab-widgets 3.0.13
kiwisolver 1.4.9
langchain 1.0.2
langchain-anthropic 1.0.0
langchain-classic 1.0.0
langchain-community 0.4.1
langchain-core 1.0.1
langchain-openai 1.0.1
langchain-text-splitters 1.0.0
langchainhub 0.1.21
langgraph 1.0.1
langgraph-checkpoint 3.0.0
langgraph-prebuilt 1.0.1
langgraph-sdk 0.2.9
langsmith 0.4.38
lazy-loader 0.4
legacy-api-wrap 1.4.1
leidenalg 0.10.2
libmambapy 2.0.5
lightning 2.5.5
lightning-utilities 0.15.2
llvmlite 0.45.1
markdown 3.9
markdown-it-py 4.0.0
marshmallow 3.26.1
matplotlib 3.10.7
matplotlib-inline 0.1.7
mdurl 0.1.2
menuinst 2.2.0
mistune 3.1.2
ml-collections 1.1.0
mpmath 1.3.0
mudata 0.3.2
multidict 6.7.0
mypy-extensions 1.1.0
narwhals 2.10.0
natsort 8.4.0
nbclient 0.10.2
nbconvert 7.16.6
nbdime 4.0.2
nbformat 5.10.4
nest-asyncio 1.6.0
networkx 3.5
nose 1.3.7
notebook-shim 0.2.4
numba 0.62.1
numcodecs 0.15.1
numpy 1.26.4
nvidia-cublas-cu12 12.8.4.1
nvidia-cuda-cupti-cu12 12.8.90
nvidia-cuda-nvrtc-cu12 12.8.93
nvidia-cuda-runtime-cu12 12.8.90
nvidia-cudnn-cu12 9.10.2.21
nvidia-cufft-cu12 11.3.3.83
nvidia-cufile-cu12 1.13.1.3
nvidia-curand-cu12 10.3.9.90
nvidia-cusolver-cu12 11.7.3.90
nvidia-cusparse-cu12 12.5.8.93
nvidia-cusparselt-cu12 0.7.1
nvidia-nccl-cu12 2.27.5
nvidia-nvjitlink-cu12 12.8.93
nvidia-nvshmem-cu12 3.3.20
nvidia-nvtx-cu12 12.8.90
obonet 1.1.1
openai 2.6.1
openpyxl 3.1.5
opt-einsum 3.4.0
optree 0.17.0
orjson 3.11.4
ormsgpack 1.11.0
overrides 7.7.0
packaging 24.2
pandas 2.3.3
pandocfilters 1.5.0
parso 0.8.4
patsy 1.0.2
pexpect 4.9.0
pickleshare 0.7.5
pillow 11.3.0
pip 24.2
pkgutil-resolve-name 1.3.10
platformdirs 4.3.6
plotly 6.3.1
pluggy 1.5.0
prometheus-client 0.21.1
prompt-toolkit 3.0.50
propcache 0.4.1
proto-plus 1.26.1
protobuf 5.29.5
psutil 6.1.1
ptyprocess 0.7.0
pure-eval 0.2.3
pyarrow 22.0.0
pyasn1 0.6.1
pyasn1-modules 0.4.2
pycosat 0.6.6
pycparser 2.22
pydantic 2.11.10
pydantic-core 2.33.2
pydantic-settings 2.11.0
pydub 0.25.1
pygments 2.19.1
pymupdf 1.26.5
pynndescent 0.5.13
pyparsing 3.2.5
pyro-api 0.1.2
pyro-ppl 1.9.1
python-dateutil 2.9.0.post0
python-dotenv 1.2.1
python-json-logger 2.0.7
python-multipart 0.0.20
pytorch-lightning 2.5.5
pytz 2025.1
pyzmq 26.2.1
rapidfuzz 3.14.1
referencing 0.36.2
regex 2025.10.23
requests 2.32.5
requests-toolbelt 1.0.0
rfc3339-validator 0.1.4
rfc3986-validator 0.1.1
rich 14.2.0
rpds-py 0.22.3
rsa 4.9.1
ruamel.yaml 0.18.10
ruamel.yaml.clib 0.2.8
ruff 0.14.2
safehttpx 0.1.7
safetensors 0.6.2
scanpy 1.11.5
scikit-image 0.25.2
scikit-learn 1.7.2
scikit-misc 0.5.1
scimilarity 0.4.1
scipy 1.16.3
scrublet 0.2.3
scvi-tools 1.4.0.post1
seaborn 0.13.2
semantic-version 2.10.0
session-info2 0.2.3
setuptools 75.1.0
shellingham 1.5.4
six 1.17.0
smmap 5.0.2
sniffio 1.3.1
solvebio 2.32.0
soupsieve 2.5
sparse 0.17.0
sqlalchemy 2.0.44
stack-data 0.6.3
starlette 0.49.1
statsmodels 0.14.5
sympy 1.14.0
tblib 3.2.0
tenacity 9.1.2
tensorboard 2.20.0
tensorboard-data-server 0.7.2
terminado 0.18.1
texttable 1.7.0
threadpoolctl 3.6.0
tifffile 2025.10.16
tiktoken 0.12.0
tiledb 0.35.1
tiledb-cloud 0.14.3
tiledb-vector-search 0.15.0
tinycss2 1.4.0
tokenizers 0.22.1
tomli 2.2.1
tomlkit 0.13.3
torch 2.9.0
torchaudio 2.9.0
torchmetrics 1.8.2
torchopt 0.7.3
torchsummary 1.5.1
torchvision 0.24.0
tornado 6.4.2
tqdm 4.66.5
traitlets 5.14.3
transformers 4.57.1
triton 3.5.0
truststore 0.9.2
typer 0.20.0
types-python-dateutil 2.9.0.20241206
types-requests 2.32.4.20250913
typing-extensions 4.15.0
typing-inspect 0.9.0
typing-inspection 0.4.2
typing-utils 0.1.0
tzdata 2025.2
umap-learn 0.5.9.post2
uri-template 1.3.0
uritemplate 4.2.0
urllib3 2.2.3
uvicorn 0.38.0
wcwidth 0.2.13
webcolors 24.11.1
webencodings 0.5.1
websocket-client 1.8.0
websockets 15.0.1
werkzeug 3.1.3
wheel 0.44.0
wrapt 1.17.3
xarray 2025.10.1
xxhash 3.6.0
yarl 1.22.0
zarr 2.18.7
zipp 3.21.0
zstandard 0.23.0
autocommand 2.2.2
backports.tarfile 1.2.0
inflect 7.3.1
jaraco.collections 5.1.0
jaraco.context 5.3.0
jaraco.functools 4.0.1
jaraco.text 3.12.1
more-itertools 10.3.0
typeguard 4.3.0

9.2 R

R Environment (R 4.4.1 )
Package Version
abind 1.4-8
ape 5.8-1
aplot 0.2.9
arrow 17.0.0.1
askpass 1.2.1
assertthat 0.2.1
aws.s3 0.3.22
aws.signature 0.6.0
backports 1.5.0
base64enc 0.1-3
beachmat 2.18.1
beeswarm 0.4.0
BH 1.87.0-1
Biobase 2.66.0
BiocGenerics 0.52.0
BiocManager 1.30.26
BiocNeighbors 1.20.2
BiocParallel 1.36.0
BiocSingular 1.18.0
BiocVersion 3.20.0
bit 4.5.0
bit64 4.5.2
bitops 1.0-9
blob 1.2.4
bluster 1.12.0
brew 1.0-10
brio 1.1.5
broom 1.0.7
bslib 0.8.0
cachem 1.1.0
Cairo 1.6-0
callr 3.7.6
caret 6.0-93
caTools 1.18.3
cellranger 1.1.0
circlize 0.4.16
classInt 0.4-10
cli 3.6.5
clipr 0.8.0
clock 0.7.1
clue 0.3-66
colorspace 2.1-1
commonmark 1.9.2
ComplexHeatmap 2.22.0
ConfigParser 1.0.0
cowplot 1.2.0
cpp11 0.5.2
crayon 1.5.3
credentials 2.0.2
crosstalk 1.2.1
curl 5.2.3
data.table 1.17.8
data.tree 1.2.0
datamods 1.5.3
DBI 1.2.3
dbplyr 2.5.0
DelayedArray 0.32.0
DelayedMatrixStats 1.28.1
deldir 2.0-4
Deriv 4.2.0
desc 1.4.3
DescTools 0.99.60
devtools 2.4.6
diagram 1.6.5
diffobj 0.3.5
digest 0.6.37
distributional 0.5.0
domino 0.3.1
DominoDataCapture 0.1.1
DominoDataR 0.2.4
doParallel 1.0.17
dotCall64 1.2
downlit 0.4.4
dplyr 1.1.4
dqrng 0.4.1
DT 0.34.0
dtplyr 1.3.1
e1071 1.7-16
edgeR 4.4.2
ellipsis 0.3.2
esquisse 1.1.2
evaluate 1.0.1
Exact 3.3
expm 1.0-0
fansi 1.0.6
farver 2.1.2
fastDummies 1.7.5
fastmap 1.2.0
fastmatch 1.1-6
feather 0.3.5
fgsea 1.35.8
fitdistrplus 1.2-4
FNN 1.1.4.1
fontawesome 0.5.2
forcats 1.0.1
foreach 1.5.2
forge 0.2.0
formatR 1.14
fs 1.6.6
futile.logger 1.4.3
futile.options 1.0.1
future 1.34.0
future.apply 1.11.2
gargle 1.5.2
generics 0.1.4
GenomeInfoDb 1.42.3
GenomeInfoDbData 1.2.13
GenomicRanges 1.58.0
gert 2.1.2
getopt 1.20.4
GetoptLong 1.0.5
ggbeeswarm 0.7.2
ggdist 3.3.3
ggfun 0.2.0
ggplot2 4.0.0
ggplotify 0.1.3
ggrastr 1.0.2
ggrepel 0.9.6
ggridges 0.5.7
gh 1.4.1
git2r 0.33.0
gitcreds 0.1.2
gld 2.6.8
glmnet 4.1-4
GlobalOptions 0.1.2
globals 0.16.3
glue 1.8.0
goftest 1.2-3
googledrive 2.1.1
googlesheets4 1.1.1
gower 1.0.1
gplots 3.2.0
gridExtra 2.3
gridGraphics 0.5-1
gridtext 0.1.5
gtable 0.3.6
gtools 3.9.5
hardhat 1.4.0
harmony 1.2.4
haven 2.5.4
hdf5r 1.3.12
here 1.0.1
hexbin 1.28.5
highr 0.11
hms 1.1.3
htmltools 0.5.8.1
htmlwidgets 1.6.4
httpuv 1.6.15
httr 1.4.7
httr2 1.0.5
ica 1.0-3
ids 1.0.1
igraph 2.2.1
ini 0.3.1
ipred 0.9-15
IRanges 2.40.1
irlba 2.3.5.1
isoband 0.2.7
iterators 1.0.14
jpeg 0.1-11
jquerylib 0.1.4
jsonlite 2.0.0
kableExtra 1.4.0
kernlab 0.9-31
knitr 1.44
ks 1.15.1
labeling 0.4.3
lambda.r 1.2.4
later 1.3.2
lava 1.8.0
lazyeval 0.2.2
leidenbase 0.1.35
lgr 0.4.4
lifecycle 1.0.4
limma 3.62.2
listenv 0.9.1
lme4 1.1-37
lmom 3.2
lmtest 0.9-40
locfit 1.5-9.12
lubridate 1.8.0
magrittr 2.0.4
markdown 1.1
MatrixGenerics 1.18.1
matrixStats 1.5.0
mclust 6.1.1
memoise 2.0.1
metapod 1.10.1
mime 0.12
miniUI 0.1.2
minqa 1.2.8
mlflow 2.19.0
ModelMetrics 1.2.2.2
modelr 0.1.11
multicool 1.0.1
munsell 0.5.1
mvtnorm 1.3-3
Nebulosa 1.16.0
neuralnet 1.44.2
nloptr 2.2.1
numDeriv 2016.8-1.1
openssl 2.2.2
optparse 1.7.5
parallelDist 0.2.7
parallelly 1.38.0
patchwork 1.3.2
pbapply 1.7-4
pbmcapply 1.5.1
pheatmap 1.0.13
phosphoricons 0.2.1
pillar 1.11.1
pkgbuild 1.4.8
pkgconfig 2.0.3
pkgdown 2.1.3
pkgload 1.4.1
plotly 4.11.0
plumber 1.2.2
plyr 1.8.9
png 0.1-8
polyclip 1.10-7
pracma 2.4.6
praise 1.0.0
prettyunits 1.2.0
pROC 1.18.5
processx 3.8.4
prodlim 2024.06.25
profvis 0.4.0
progress 1.2.3
progressr 0.17.0
promises 1.3.0
proxy 0.4-27
ps 1.8.0
purrr 1.1.0
quadprog 1.5-8
R.methodsS3 1.8.2
R.oo 1.26.0
R.utils 2.13.0
R6 2.6.1
ragg 1.5.0
randomForest 4.7-1.1
RANN 2.6.2
rappdirs 0.3.3
rbibutils 2.3
rcmdcheck 1.4.0
RColorBrewer 1.1-3
Rcpp 1.1.0
Rcpp11 3.1.2.0.1
RcppAnnoy 0.0.22
RcppArmadillo 15.0.2-2
RcppEigen 0.3.4.0.2
RcppHNSW 0.6.0
RcppParallel 5.1.11-1
RcppProgress 0.4.2
RcppTOML 0.2.2
RCurl 1.98-1.17
Rdpack 2.6.4
reactable 0.4.4
reactR 0.6.1
readr 2.1.5
readxl 1.4.5
recipes 1.1.0
reformulas 0.4.2
rematch 2.0.0
rematch2 2.1.2
remotes 2.5.0
reprex 2.1.1
reshape2 1.4.4
reticulate 1.44.0
rgdal 1.5-32
RhpcBLASctl 0.23-42
rio 1.2.3
rJava 1.0-11
RJDBC 0.2-10
rjson 0.2.23
RJSONIO 2.0.0
rlang 1.1.6
rmarkdown 2.28
ROCR 1.0-11
rootSolve 1.8.2.4
roxygen2 7.3.3
rprojroot 2.1.1
RSpectra 0.16-2
rstudioapi 0.16.0
rsvd 1.0.5
Rtsne 0.17
rversions 2.1.2
rvest 1.0.4
S4Arrays 1.6.0
S4Vectors 0.44.0
S7 0.2.0
sass 0.4.9
ScaledMatrix 1.10.0
scales 1.4.0
scattermore 1.2
scran 1.30.2
sctransform 0.4.2
scuttle 1.12.0
selectr 0.4-2
sessioninfo 1.2.3
Seurat 5.3.0
SeuratObject 5.2.0
shape 1.4.6.1
shiny 1.9.1
shinybusy 0.3.3
shinyWidgets 0.8.7
SingleCellExperiment 1.28.1
sitmo 2.0.2
snow 0.4-4
sodium 1.3.2
sourcetools 0.1.7-1
sp 2.1-4
spam 2.11-1
SparseArray 1.6.2
sparseMatrixStats 1.18.0
spatstat.data 3.1-9
spatstat.explore 3.5-3
spatstat.geom 3.6-0
spatstat.random 3.4-2
spatstat.sparse 3.1-0
spatstat.univar 3.1-4
spatstat.utils 3.2-0
SQUAREM 2021.1
statmod 1.5.1
stringi 1.8.7
stringr 1.5.2
SummarizedExperiment 1.36.0
svglite 2.2.2
swagger 5.17.14.1
sys 3.4.3
systemfonts 1.3.1
tensor 1.5.1
testthat 3.2.3
textshaping 0.4.0
tibble 3.3.0
tidyr 1.3.1
tidyselect 1.2.1
tidytree 0.4.6
tidyverse 1.3.2
timechange 0.3.0
timeDate 4041.110
tinytex 0.53
toastui 0.3.4
treeio 1.26.0
triebeard 0.4.1
tzdb 0.4.0
UCSC.utils 1.2.0
urlchecker 1.0.1
urltools 1.7.3
usethis 3.2.1
utf8 1.2.6
uuid 1.2-1
uwot 0.2.3
vcd 1.4-10
vctrs 0.6.5
versions 0.3
vipor 0.4.7
viridis 0.6.5
viridisLite 0.4.2
visNetwork 2.1.4
vroom 1.6.5
waldo 0.6.2
webutils 1.2.2
whisker 0.4.1
withr 3.0.2
writexl 1.5.1
xfun 0.48
xml2 1.3.6
xopen 1.0.1
xtable 1.8-4
XVector 0.46.0
yaGST 2017.08.25
yaml 2.3.10
yulab.utils 0.2.1
zeallot 0.1.0
zip 2.3.1
zlibbioc 1.52.0
zoo 1.8-12
base 4.4.1
boot 1.3-30
class 7.3-22
cluster 2.1.6
codetools 0.2-19
compiler 4.4.1
datasets 4.4.1
foreign 0.8-86
graphics 4.4.1
grDevices 4.4.1
grid 4.4.1
KernSmooth 2.23-24
lattice 0.22-5
MASS 7.3-61
Matrix 1.6-5
methods 4.4.1
mgcv 1.9-1
nlme 3.1-165
nnet 7.3-19
parallel 4.4.1
rpart 4.1.23
spatial 7.3-15
splines 4.4.1
stats 4.4.1
stats4 4.4.1
survival 3.7-0
tcltk 4.4.1
tools 4.4.1
utils 4.4.1

9.3 Docker

Container Build Metadata
Key Value
image_repo astrazeneca/cellatria
image_tag v1.0.0
image_version v1.0.0
vcs_ref e63e5b84a47e951d98ff1ba00ce510756ca1ad88
build_date 2025-11-04T00:12:09Z
tree_state clean